# Context-Enhanced Vulnerability Detection Based on Large Language Model

arXiv: https://arxiv.org/abs/2504.16877 · HTML: https://mlsanity.com/paper/2504.16877/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2025.

## Built on by (most cited first, 6 of 6)

- [A Systematic Literature Review on Detecting Software Vulnerabilities with Large Language Models](https://mlsanity.com/paper/2507.22659/) (2025)
- [VulInstruct: Teaching LLMs Root-Cause Reasoning for Vulnerability Detection via Security Specifications](https://mlsanity.com/paper/2511.04014/) (2025)
- [DREA: Decoupled Reasoning and Exploration Agents for Repository-Level Vulnerability Detection](https://mlsanity.com/paper/2607.13439/) (2026)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [From LLMs to Agents: A Comparative Evaluation of LLMs and LLM-based Agents in Security Patch Detection](https://mlsanity.com/paper/2511.08060/) (2025)
- [Knowledge-Enhanced Agentic Vulnerability Repair](https://mlsanity.com/paper/2607.00820/) (2026)

## Abstract (arXiv)

Vulnerability detection is a critical aspect of software security. Accurate detection is essential to prevent potential security breaches and protect software systems from malicious attacks. Recently, vulnerability detection methods leveraging deep learning and large language models (LLMs) have garnered increasing attention. However, existing approaches often focus on analyzing individual files or functions, which limits their ability to gather sufficient contextual information. Analyzing entire repositories to gather context introduces significant noise and computational overhead. To address these challenges, we propose a context-enhanced vulnerability detection approach that combines program analysis with LLMs. Specifically, we use program analysis to extract contextual information at various levels of abstraction, thereby filtering out irrelevant noise. The abstracted context along with source code are provided to LLM for vulnerability detection. We investigate how different levels of contextual granularity improve LLM-based vulnerability detection performance. Our goal is to strike a balance between providing sufficient detail to accurately capture vulnerabilities and minimizing unnecessary complexity that could hinder model performance. Based on an extensive study using GPT-4, DeepSeek, and CodeLLaMA with various prompting strategies, our key findings includes: (1) incorporating abstracted context significantly enhances vulnerability detection effectiveness; (2) different models benefit from distinct levels of abstraction depending on their code understanding capabilities; and (3) capturing program behavior through program analysis for general LLM-based code analysis tasks can be a direction that requires further attention.